Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/vbcherepanov/total-agent-memory/cursor-rulesgit clone --depth 1 https://github.com/vbcherepanov/total-agent-memoryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00499 | $0.00499 |
| Opus 5 | $0.00249 | $0.00249 |
| Sonnet 5 | $0.00100 | $0.00100 |
| Haiku 4.5 | $0.00050 | $0.00050 |
Grade A, and why
cursor-rules scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Memory Protocol (total-agent-memory v10.5)
You have access to a persistent cross-session memory via the
memory MCP server. Knowledge survives between sessions and is
shared across agents working on the same project.
The five non-negotiables
- Session start →
session_initfirst, thenmemory_recall. - Before any non-trivial task →
memory_recall(query, project). - After every significant action →
memory_saveimmediately. - On error / stuck →
learn_error(orself_error_log). - End of session →
session_endwith summary + next_steps + pitfalls.
Save discipline
ЧТО: one-line summary
ПРОЕКТ: project name
ФАЙЛЫ: absolute paths to the key files
СТЕК: language / framework / version
ПОДХОД: 3–7 key steps
НЮАНСЫ: gotchas, edge cases
For decisions, add WHY (alternatives, trade-offs).
Tags must include ["reusable", "<tech>"] if the recipe applies elsewhere.
Recall discipline
memory_recall(query, project=<current>)— current project first.- Empty / contradicting →
analogize(text=query, exclude_project=...). - Still empty → context7 / WebSearch / first principles.
- Never guess a convention you can recall.
Self-improvement
- 3 identical errors → STOP.
memory_save(type='lesson', stuck=...)+ ask the user with A/B/C options. - Reusable solution →
memory_save(tags=['reusable', ...]).
Anti-patterns
- Don't ask "should I save this?" — save without asking.
- Don't batch saves until end of session — save now.
- Don't recall and ignore — cite the id + 1-line gist before applying.
- Don't save terminal dumps — distill to the digest above.
- Don't guess a convention you can recall.
For the full tool reference, hooks documentation, and per-IDE setup
see ~/total-agent-memory/skills/memory-protocol/references/.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 59 lines · 499 tokens per session scan A f3d5b9b29194
cursor-rules is a cursor rule published in the GitHub repository vbcherepanov/total-agent-memory (66 stars, last pushed 4d ago), licensed MIT. It adds 499 tokens to every session, about $0.0025 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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